What did we learn about elite student-athlete mental health systems from the COVID-19 pandemic?
Bibliographic record
Abstract
Elite student-athletes (SAs) in higher education (HE) have distinct mental health (MH) risks. The COVID-19 pandemic put pressure on systems and increased elite SA vulnerability to adverse MH outcomes. The aim of this study was to explore the provision and management of MH in elite HE sports settings during the time of COVID-19 pandemic stress. The secondary aim was to identify lessons and opportunities to enhance future mental healthcare systems and services for elite SAs. A qualitative study design was used to investigate the views of three groups (athletic directors, coaches and sport healthcare providers). Ten key leaders were purposively recruited from HE institutions in Canada, the USA and the United Kingdom. They represented various universities from the National College Athletic Association, U SPORTS Canada and British Universities and Colleges Sport. Semistructured interviews were conducted, recorded, transcribed and thematically analysed. Five key themes were identified: (1) The pandemic disruption had salient impacts on motivation and how elite SAs engaged with sport (2) when student sport systems are under pressure, support staff perceive a change in duties and experience their own MH challenges, (3) the pandemic increased awareness about MH care provision and exposed systemic challenges, (4) digital transformation in MH is complex and has additional challenges for SAs and (5) there were some positive outcomes of the pandemic, lessons learnt and a resulting motivation for systems change. Participants highlighted future opportunities for MH provision in elite university sport settings. Four recommendations were generated from the results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".